Interactive effects between cover crop management and the environment modulate benefits to cash crop yields: a meta-analysis
Bibliographic record
Abstract
Several governmental programs have been established throughout Canada to foster agriculture sustainability. As a best management practice, cover crops (CCs) limit soil erosion and prevent nutrient losses in agroecosystems. Yet, the variable effects of CCs on cash crop productivity previously reported may limit their large-scale adoption by farmers. To address this variability, we conducted an unweighted meta-analysis including 2274 observations from 86 field studies conducted under humid temperate climate to evaluate yield response to CCs for three annual cash crops. Overall, CCs increased corn and small grain cereal yields by 13% and 22% respectively, but did not affect soybean yield. Legume CCs alone or mixed with grasses provided the highest small grain cereal and corn yield increases compared with non-legume broadleaf and grass CCs. CC benefits increased with nitrogen (N) content in CC aboveground biomass but decreased when N fertilizer inputs applied to corn exceeded 60 kg N ha −1 . Greater precipitation and N fertilizer inputs reduced the negative effect of grass CCs on corn yield, while benefits of legume CCs were highly resilient to precipitation variations. CC benefits on corn yield increased through time and at low soil organic matter content, especially at low N fertilizer inputs. These results evidence the complex interplay between cash crop productivity, CC management, and environmental factors — related to N inputs from CCs, changes in soil properties (e.g., increased organic matter, improved soil structure or microbial activity), or potential competition for water under drier conditions — which provide new perspectives to promote CC inclusion in cropping systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.049 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".